pySCENIC is a lightning-fast python implementation of the SCENIC pipeline (Single-Cell rEgulatory Network Inference and Clustering) which enables biologists to infer transcription factors, gene regulatory networks and cell types from single-cell RNA-seq data.

[ICLR 2024] DNABERT-2: Efficient Foundation Model and Benchmark for Multi-Species Genome

Single-cell Transcriptome and Regulome Analysis Pipeline

netZooR is a network biology package implemented in R.

netZooPy is a network biology package implemented in Python.

:dart: Human transcription factor target genes from 6 databases in convenient R format.

Netbooks is a JupyterHub catalog of use cases in gene regulatory network inference using netZoo methods..

pyJASPAR: A Pythonic interface to JASPAR transcription factor motifs

Python Toolkit for Transcription Factor Activity Inference and Clustering of scRNA-seq Data

BITFAM is a Bayesian approach and platform to infer transcription factor activities within individual cells using single cell RNA-sequencing data. Please see Gao S et al., Genome Research (2021) https://genome.cshlp.org/content/31/7/1296 for details.

netZooM is a network biology package implemented in MATLAB.

Python script to quickly extract promoter and terminator regions with the NCBI API. Search for the presence of individual pattern or transcription factor responsive elements with manual sequence (IUPAC) or JASPAR API.

Single Cell Oriented Reconstruction of PANDA-based Individually Optimized Networks

:bug: How to use CENTIPEDE to determine if a transcription factor is bound.

netZooC is a network biology package implemented in C.

Parse TF motifs from public databases, read into R, and scan using 'rtfbs'.

A motif discovery tool to detect the occurrences of known motifs

Read HOMER motif analysis output in R.

Deep neural networks implemented in TensorFlow & Python for predicting whether transcription factors will bind to given DNA sequences

No description provided.

Bioinformatics pipeline to identify differentially active transcription factors between conditions using expression and epigenetic data

A “data light” TF-network mapping algorithm using only gene expression and genome sequence data.

7C: Computational Chromosome Conformation Capture by Correlation of ChIP-seq at CTCF motifs

MYB transcription factors are one of the largest gene family in plants and control many processes. This repository provides additional background to the #MYB_Monday tweets

Codebase for the domain adaptation (cross-species TF binding prediction) project.

Bioinformatics pipeline that makes use of expression and open chromatin data to identify differentially active transcription factors across conditions.

Bioinformatic approach to identify functional transcription factor binding motifs

Pipeline for predicting ChIP-seq peaks in novel cell types using chromatin accessibility

:m: Tool for motif conservation analysis

Network Regression Embeddings reveal cell-type Transcription Factor coordination for target gene (TG) regulation